Patterns and predictors of suicides in the U.S. Army: An introduction to the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

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1 Patterns and predictors of suicides in the U.S. Army: An introduction to the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) Ronald C. Kessler, Ph.D. Harvard Medical School March 2014 Slide 1

2 Disclosures Dr. Kessler has been a consultant for AstraZeneca, Analysis Group, Bristol-Myers Squibb, Cerner-Galt Associates, Eli Lilly & Company, GlaxoSmithKline Inc., HealthCore Inc., Health Dialog, Hoffman-LaRoche, Inc., Integrated Benefits Institute, J & J Wellness & Prevention, Inc., John Snow Inc., Kaiser Permanente, Lake Nona Institute, Matria Inc., Mensante, Merck & Co, Inc., Ortho-McNeil Janssen Scientific Affairs, Pfizer Inc., Primary Care Network, Research Triangle Institute, Sanofi-Aventis Groupe, Shire US Inc., SRA International, Inc., Takeda Global Research & Development, Transcept Pharmaceuticals Inc., and Wyeth-Ayerst; has served on advisory boards for Appliance Computing II, Eli Lilly & Company, Mindsite, Ortho-McNeil Janssen Scientific Affairs, Johnson & Johnson, Plus One Health Management and Wyeth-Ayerst; has had research support for his epidemiological studies from Analysis Group Inc., Bristol- Myers Squibb, Eli Lilly & Company, EPI-Q, GlaxoSmithKline, Johnson & Johnson Pharmaceuticals, Ortho-McNeil Janssen Scientific Affairs., Pfizer Inc., Sanofi-Aventis Groupe, Shire US, Inc., and Walgreens Co.; and owns 25% share in DataStat, Inc. Slide 2

3 Administration & Funding Supported by NIH Cooperative Agreement U01MH Funds provided by the Department of the Army ($50M) & National Institute of Mental Health (NIMH: $15M). Army STARRS is being conducted by a consortium of investigators from the Uniformed Services University of the Health Sciences (USUHS), the University of California-San Diego (UCSD), Harvard Medical School (HMS), & the University of Michigan (UM) in collaboration with the NIMH. Co-PIs: R. Ursano (USUHS) & M. Stein (UCSD) Site-PIs: R. Kessler (HMS) & S. Heeringa (UM) Collaborating NIMH Scientists: L. Colpe & M. Schoenbaum Consulting Army Scientists: K. Cox & S. Cersovsky Army STARRS in-theater research was conducted under a protocol reviewed and approved by the U.S. Army Medical Research and Materiel Command (MRMC) Institutional Review Board, and in accordance with the approved protocol. Slide 3

4 Distribution of suicide by rank and time in service, all Regular Army Soldiers Never deployed Currently deployed Previously deployed l. Suicides/100,000 person-years A. Enlisted 0-4 years of service years of service B. Officers 0-4 years of service years of service Il. Percent of all Regular Army Soldiers/suicides A. Enlisted 0-4 years of service 22.6/ / / years of service 11.2/ / /25.0 B. Officers 0-4 years of service 1.7/ / / years of service 4.5/ / /3.2 Gilman et al. in press Psychological Medicine. Slide 4

5 Basic descriptive correlates of suicide among enlisted Regular Army Soldiers Population attributable risk Never married among currently deployed 32.4% Lower-than-expected junior enlisted rank Never deployed (0-4 years of service) 20.3% Currently deployed (0-4 years of service) 14.9% Previously deployed (0-4 years of service) 13.6% Gilman et al. in press Psychological Medicine. Slide 5

6 Using administrative data to predict posthospital suicides among Regular Army Soldiers : Basic distribution of annual inpatient and outpatient contacts for ICD-9 mental or substance disorders and suicide rates Percent of all soldiers Percent of all suicides Suicides/ 100,000 person-years Inpatient Outpatient All others Slide 6

7 Using administrative data to predict post-hospital suicides among Regular Army Soldiers : Procedures Army/DoD administrative data available at time of hospitalization used to predict Demographic, administrative, medical, criminal justice Intake diagnosis and information on suicidality as a reason for admission Suicides predicted in 12 months after hospital discharge. Machine learning methods used to predict Random forests Penalized regression (ridge, lasso, elastic net) Super learner Slide 7

8 Percent of all post-hospital suicides 70 Using administrative data to predict posthospital suicides among Regular Army Soldiers : Concentration of observed risk across ventiles of predicted risk Psychiatric primary, specialty care Psychiatric primary, general medical care Physical primary Top Highest 5% Risk Lowest Bottom Risk 5% Ventiles of predicted suicide risk Slide 8

9 Using administrative data to predict posthospital suicides among Regular Army Soldiers : Interpretation 63% of all post-hospital suicides occurred among the 10% of patients with highest predicted risk. High-risk patients also had elevated risks of accidental deaths, suicide attempts, and a number of other adverse outcomes in the year after hospital discharge. The numbers are small enough, and the risks high enough to warrant targeting an enhanced post-discharge preventive intervention program if results hold up in replication in more recent years. That analysis of validation is currently underway. Slide 9

10 Predicting Army suicides: Where do we go from here? Developing risk algorithms for other high-risk sub-populations (e.g., outpatients, soon-to-deploy, etc.). The two tasks of prediction CQI related to the first task Quasi-experimental policy simulation related to the second task Evaluation of interventions Slide 10

This continuing education activity is co-sponsored by Indiana University School of Medicine and by CME Outfitters, LLC.

This continuing education activity is co-sponsored by Indiana University School of Medicine and by CME Outfitters, LLC. Indiana University School of Medicine and CME Outfitters, LLC, gratefully acknowledge